Whole exome sequencing and machine learning germline analysis of individuals presenting with extreme phenotypes of high and low risk of developing tobacco-associated lung adenocarcinoma.

BACKGROUND: Tobacco is the main risk factor for developing lung cancer. Yet, while some heavy smokers develop lung cancer at a young age, other heavy smokers never develop it, even at an advanced age, suggesting a remarkable variability in the individual susceptibility to the carcinogenic effects of tobacco. We characterized the germline profile of subjects presenting these extreme phenotypes with Whole Exome Sequencing (WES) and Machine Learning (ML). METHODS: We sequenced germline DNA from heavy smokers who either developed lung adenocarcinoma at an early age (extreme cases) or who did not develop lung cancer at an advanced age (extreme controls), selected from databases including over 6600 subjects. We selected individual coding genetic variants and variant-rich genes showing a significantly different distribution between extreme cases and controls. We validated the results from our discovery cohort, in which we analysed by WES extreme cases and controls presenting similar phenotypes. We developed ML models using both cohorts. FINDINGS: Mean age for extreme cases and controls was 50.7 and 79.1 years respectively, and mean tobacco consumption was 34.6 and 62.3 pack-years. We validated 16 individual variants and 33 variant-rich genes. The gene harbouring the most validated variants was HLA-A in extreme controls (4 variants in the discovery cohort, p = 3.46E-07; and 4 in the validation cohort, p = 1.67E-06). We trained ML models using as input the 16 individual variants in the discovery cohort and tested them on the validation cohort, obtaining an accuracy of 76.5% and an AUC-ROC of 83.6%. Functions of validated genes included candidate oncogenes, tumour-suppressors, DNA repair, HLA-mediated antigen presentation and regulation of proliferation, apoptosis, inflammation and immune response. INTERPRETATION: Individuals presenting extreme phenotypes of high and low risk of developing tobacco-associated lung adenocarcinoma show different germline profiles. Our strategy may allow the identification of high-risk subjects and the development of new therapeutic approaches. FUNDING: See a detailed list of funding bodies in the Acknowledgements section at the end of the manuscript.

Comparison of Myocardial Blood Flow Quantification Models for Double ECG Gating Arterial Spin Labeling MRI: Reproducibility Assessment.

BACKGROUND: Arterial spin labeling (ASL) allows non-invasive quantification of myocardial blood flow (MBF). Double-ECG gating (DG) ASL is more robust to heart rate variability than single-ECG gating (SG), but its reproducibility requires further investigation. Moreover, the existence of multiple quantification models hinders its application. Frequency-offset-corrected-inversion (FOCI) pulses provide sharper edge profiles than hyperbolic-secant (HS), which could benefit myocardial ASL. PURPOSE: To assess the performance of MBF quantification models for DG compared to SG ASL, to evaluate their reproducibility and to compare the effects of HS and FOCI pulses. STUDY TYPE: Prospective. SUBJECTS: Sixteen subjects (27 ± 8 years). FIELD STRENGTH/SEQUENCE: 1.5 T/DG and SG flow-sensitive alternating inversion recovery ASL. ASSESSMENT: Three models for DG MBF quantification were compared using Monte Carlo simulations and in vivo experiments. Two models used a fitting approach (one using only a single label and control image pair per fit, the other using all available image pairs), while the third model used a T1 correction approach. Slice profile simulations were conducted for HS and FOCI pulses with varying B0 and B1. Temporal signal-to-noise ratio (tSNR) was computed for different acquisition/quantification strategies and inversion pulses. The number of images that minimized MBF error was investigated in the model with highest tSNR. Intra and intersession reproducibility were assessed in 10 subjects. STATISTICAL TESTS: Within-subject coefficient of variation, analysis of variance. P-value <0.05 was considered significant. RESULTS: MBF was not different across acquisition/quantification strategies (P = 0.27) nor pulses (P = 0.9). DG MBF quantification models exhibited significantly higher tSNR and superior reproducibility, particularly for the fitting model using multiple images (tSNR was 3.46 ± 2.18 in vivo and 3.32 ± 1.16 in simulations, respectively; wsCV = 16%). Reducing the number of ASL pairs to 13/15 did not increase MBF error (minimum = 0.22 mL/g/min). DATA CONCLUSION: Reproducibility of MBF was better for DG than SG acquisitions, especially when employing a fitting model. LEVEL OF EVIDENCE: 2 TECHNICAL EFFICACY: Stage 1.

Diagnostic and Prognostic Potential of Multiparametric Renal MRI in Kidney Transplant Patients.

BACKGROUND: Multiparametric MRI provides assessment of functional and structural parameters in kidney allografts. It offers a non-invasive alternative to the current reference standard of kidney biopsy. PURPOSE: To evaluate the diagnostic and prognostic utility of MRI parameters in the assessment of allograft function in the first 3-months post-transplantation. STUDY TYPE: Prospective. SUBJECTS: 32 transplant recipients (54 ± 17 years, 20 females), divided into two groups according to estimated glomerular filtration rate (eGFR) at 3-months post-transplantation: inferior graft function (IGF; eGFR<45 mL/min/1.73 m2 , n = 10) and superior graft function (SGF; eGFR ≥ 45 mL/min/1.73 m2 , n = 22). Further categorization was based on the need for hemodialysis (C1) and decrease in s-creatinine (C2) at 1-week post-transplantation: delayed-graft-function (DGF: n = 4 C1, n = 10 C2) and early graft-function (EGF: n = 28 C1, n = 22 C2). FIELD STRENGTH/SEQUENCE: 3-T, pseudo-continuous arterial spin labeling, T1-mapping, and diffusion-weighted imaging. ASSESSMENT: Multiparametric MRI was evaluated at 1-week in all patients and 3-months after transplantation in 28 patients. Renal blood flow (RBF), diffusion coefficients (ADC, ΔADC, D, ∆ $$ \Delta $$ D, D*, flowing fraction f), T1 and ∆ $$ \Delta $$ T1 were calculated in cortex and medulla. The diagnostic and prognostic value of these parameters, obtained at 3-months and 1-week post-transplantation, respectively, was evaluated in the cortex to discriminate between DGF and EGF, and between SGF and IGF. STATISTICAL TESTS: Logistic regression, receiver-operating-characteristics, area-under-the-curve (AUC), confidence intervals (CIs), analysis-of-variance, t-test, Wilcoxon-Mann-Whitney test, Fisher's exact test, Pearson's correlation. P-value<0.05 was considered significant. RESULTS: DGF patients exhibited significantly lower cortical RBF and f and higher D*. The diagnostic value of MRI for detecting DGF was excellent (AUC = 100%). Significant differences between patients with IGF and SGF were found in RBF, ∆T1 , and ∆D. Multiparametric MRI showed higher diagnostic (AUC = 95.32%; CI: 88%-100%) and prognostic (AUC = 97.47%, CI: 92%-100%) values for detecting IGF than eGFR (AUC = 89.50%, CI: 79%-100%). DATA CONCLUSION: Multiparametric MRI may show high diagnostic and prognostic value in transplanted patients, yielding better results compared to eGFR measurements. LEVEL OF EVIDENCE: 2 TECHNICAL EFFICACY: Stage 1.

Lung Cancer Screening in People With COPD: The Pamplona-IELCAP Experience.

INTRODUCTION: The Global Initiative for Obstructive Lung Disease (GOLD) recommends lung cancer screening for patients with Chronic Obstructive Pulmonary Disease (COPD), but data is lacking regarding results of screening in this high-risk population. The main goal of the present work is to explore if lung cancer screening with Low Dose Chest Tomography (LDCT) in people with COPD, allows lung cancer (LC) diagnosis in early stages with survival compatible with curative state. METHODS: This is a post hoc exploratory analysis. Pamplona International Early Lung Cancer Action Program (P-IELCAP) participants with a GOLD defined obstructive pattern (post bronchodilator FEV1/FVC<0.70) were selected for analysis. The characteristics of those who developed LC and their survival are described. A Cox proportional analysis explored the factors associated with LC diagnosis. RESULTS: Eight hundred and sixty-five patients (77% male, 93% in spirometric GOLD stage 1+2) were followed for 102±63 months. LC prevalence was 2.6% at baseline, with an annual LC diagnosis rate of 0.68%. Early-stage tumors predominated (74%) with a median survival (25-75th percentiles) of 139 (76-185) months. Cumulative tobacco exposure, FEV1%, and emphysema were the main predictors of an LC diagnosis. Eight (11%) patients with COPD had a second LC, most of them in early stage (92%), and 6 (8%) had recurrence. Median survival (25-75th percentiles) in these patients was 168 (108-191) months. CONCLUSIONS: Lung cancer screening of selected high-risk participants with COPD allowed the LC diagnosis in early stages with survival compatible with curative state.

Impact of Applying the Global Lung Initiative Criteria for Airway Obstruction in GOLD Defined COPD Cohorts: The BODE and CHAIN Experience.

INTRODUCTION: The Global Lung Function Initiative (GLI) has proposed new criteria for airflow limitation (AL) and recommends using these to interpret spirometry. The objective of this study was to explore the impact of the application of the AL GLI criteria in two well characterized GOLD-defined COPD cohorts. METHODS: COPD patients from the BODE (n=360) and the COPD History Assessment In SpaiN (CHAIN) cohorts (n=722) were enrolled and followed. Age, gender, pack-years history, BMI, dyspnea, lung function measurements, exercise capacity, BODE index, history of exacerbations and survival were recorded. CT-detected comorbidities were registered in the BODE cohort. The proportion of subjects without AL by GLI criteria was determined in each cohort. The clinical, CT-detected comorbidity, and overall survival of these patients were evaluated. RESULTS: In total, 18% of the BODE and 15% of the CHAIN cohort did not meet GLI AL criteria. In the BODE and CHAIN cohorts respectively, these patients had a high clinical burden (BODE≥3: 9% and 20%; mMRC≥2: 16% and 45%; exacerbations in the previous year: 31% and 9%; 6MWD<350m: 15% and 19%, respectively), and a similar prevalence of CT-diagnosed comorbidities compared with those with GLI AL. They also had a higher rate of long-term mortality - 33% and 22% respectively. CONCLUSIONS: An important proportion of patients from 2 GOLD-defined COPD cohorts did not meet GLI AL criteria at enrolment, although they had a significant burden of disease. Caution must be taken when applying the GLI AL criteria in clinical practice.

A deep learning image analysis method for renal perfusion estimation in pseudo-continuous arterial spin labelling MRI.

Accurate segmentation of renal tissues is an essential step for renal perfusion estimation and postoperative assessment of the allograft. Images are usually manually labeled, which is tedious and prone to human error. We present an image analysis method for the automatic estimation of renal perfusion based on perfusion magnetic resonance imaging. Specifically, non-contrasted pseudo-continuous arterial spin labeling (PCASL) images are used for kidney transplant evaluation and perfusion estimation, as a biomarker of the status of the allograft. The proposed method uses machine/deep learning tools for the segmentation and classification of renal cortical and medullary tissues and automates the estimation of perfusion values. Data from 16 transplant patients has been used for the experiments. The automatic analysis of differentiated tissues within the kidney, such as cortex and medulla, is performed by employing the time-intensity-curves of non-contrasted T1-weighted MRI series. Specifically, using the Dice similarity coefficient as a figure of merit, results above 93%, 92% and 82% are obtained for whole kidney, cortex, and medulla, respectively. Besides, estimated cortical and medullary perfusion values are considered to be within the acceptable ranges within clinical practice.

Sex-based differences in late gadolinium enhancement among patients with acute myocarditis.

OBJECTIVE: The aims of our study were to investigate the sex differences in late gadolinium enhancement (LGE) using cardiovascular magnetic resonance (CMR) in a single-centre cohort of consecutive patients with acute myocarditis (AM). METHOD: This retrospective study performed CMR scans in 135 consecutive patients with AM that met the Lake Louise criteria. On CMR, LV ventricular strain functions were performed on conventional cine SSFP sequences. Besides myocardial strain measurements, myocardial scar location, extension, and size were assigned and quantified by LGE imaging. RESULTS: There was no difference in age (age 42.51 ± 19.64 years vs 40.92 ± 19.94 years; p = 0.74) and cardiovascular risk profile between women and men. Despite similar comorbidities, women were more like to present with dyspnea (p = 0.001). Women demonstrated higher prevalence of septal LGE (p = 0.004) and increased global circumferential strain parameters (p = 0.008) in comparison with men. In multivariate analysis, female sex remained an independent determinant of septal LGE (ß coefficient = -0.520, p = 0.001). CONCLUSION: This is the first study reporting sex differences in LGE localization in AM. Women have more septal LGE involvement independent of age, cardiovascular risk factors, and CMR parameters. These findings further emphasize the sex-based differences in cardiovascular diseases.

Safety, feasibility, and hemodynamic response of regadenoson for stress perfusion CMR.

Owing to its pharmacodynamics and posology, the use of regadenoson for stress cardiac magnetic resonance (CMR) has potential advantages over other vasodilators. We sought to evaluate the safety, hemodynamic response and diagnostic performance of regadenoson stress-CMR in routine clinical practice. All regadenoson stress-CMR examinations performed between May 2017 and July 2020 at our institution were retrospectively reviewed. A total of 698 studies were included for the final analysis. A conventional stress/rest protocol was performed using a 1.5T MRI scanner (Magnetom Aera, Siemens Healthineers, Erlangen, Germany). Adverse events, clinical symptoms, and hemodynamic response were assessed. Diagnostic accuracy of the test was evaluated in patients who underwent invasive coronary angiography. Nearly half of patients (48.5%) remained asymptomatic. Most common clinical symptoms included dyspnea (137, 19.6%), chest pain (116, 16.6%) and flushing (44, 6.3%). Two patients (0.28%) could not complete the examination due to severe hypotension or unbearable chest pain. Overall, an increase in heart rate (HR) response (36.2% [IQR: 22.5?50.9]) and a decrease in systolic and diastolic blood pressure (BP) (median systolic BP response of -5% [IQR: -11.5-0.6]; median diastolic BP response of -6.3 mmHg [IQR: -13.4-0]) was observed. Patients with symptoms induced by regadenoson showed higher HR response (40.3%, IQR: 26.4?56.1 vs. 32.4%, IQR: 19-45.6, p < 0.001), whereas a blunted HR response was observed in diabetic (29.6%, IQR: 18.4?42 p < 0.001), obese (31.7%, IQR: 20.7?46.2 p = 0.005) and patients aged 70 years or older (32.9%, IQR: 22.6?43.1 p < 0.001). Overall, regadenoson stress-CMR showed 95.65% (IQ 91.49?99.81) sensitivity, 54.84% (IQ 35.71?73.97) specificity, 86.99% (IQ 82.74?94.68) positive predictive value, and 77.27% (IQ 57.49?97.06) negative predictive value for detecting significant coronary stenosis as compared with invasive coronary angiography. Regadenoson is a well-tolerated vasodilator that can be safely employed for stress perfusion CMR, with high diagnostic performance.

Exploring the EVolution in PrognOstic CapabiLity of MUltisequence Cardiac MagneTIc ResOnance in PatieNts Affected by Takotsubo Cardiomyopathy Based on Machine Learning Analysis: Design and Rationale of the EVOLUTION Study.

PURPOSE: Takotsubo cardiomyopathy (TTC) is a transient but severe acute myocardial dysfunction with a wide range of outcomes from favorable to life-threatening. The current risk stratification scores of TTC patients do not include cardiac magnetic resonance (CMR) parameters. To date, it is still unknown whether and how clinical, trans-thoracic echocardiography (TTE), and CMR data can be integrated to improve risk stratification. METHODS: EVOLUTION (Exploring the eVolution in prognOstic capabiLity of mUlti-sequence cardiac magneTIc resOnance in patieNts affected by Takotsubo cardiomyopathy) is a multicenter, international registry of TTC patients who will undergo a clinical, TTE, and CMR evaluation. Clinical data including demographics, risk factors, comorbidities, laboratory values, ECG, and results from TTE and CMR analysis will be collected, and each patient will be followed-up for in-hospital and long-term outcomes. Clinical outcome measures during hospitalization will include cardiovascular death, pulmonary edema, arrhythmias, stroke, or transient ischemic attack.Clinical long-term outcome measures will include cardiovascular death, pulmonary edema, heart failure, arrhythmias, sudden cardiac death, and major adverse cardiac and cerebrovascular events defined as a composite endpoint of death from any cause, myocardial infarction, recurrence of TTC, transient ischemic attack, and stroke. We will develop a comprehensive clinical and imaging score that predicts TTC outcomes and test the value of machine learning models, incorporating clinical and imaging parameters to predict prognosis. CONCLUSIONS: The main goal of the study is to develop a comprehensive clinical and imaging score, that includes TTE and CMR data, in a large cohort of TTC patients for risk stratification and outcome prediction as a basis for possible changes in patient management.

Magnetic resonance in patients with cardiovascular devices. SEC-GT CRMTC/SEC-Heart Rhythm Association/SERAM/SEICAT consensus document.

Magnetic resonance has become a first-line imaging modality in various clinical scenarios. The number of patients with different cardiovascular devices, including cardiac implantable electronic devices, has increased exponentially. Although there have been reports of risks associated with exposure to magnetic resonance in these patients, the clinical evidence now supports the safety of performing these studies under specific conditions and following recommendations to minimize possible risks. This document was written by the Working Group on Cardiac Magnetic Resonance Imaging and Cardiac Computed Tomography of the Spanish Society of Cardiology (SEC-GT CRMTC), the Heart Rhythm Association of the Spanish Society of Cardiology (SEC-Heart Rhythm Association), the Spanish Society of Medical Radiology (SERAM), and the Spanish Society of Cardiothoracic Imaging (SEICAT). The document reviews the clinical evidence available in this field and establishes a series of recommendations so that patients with cardiovascular devices can safely access this diagnostic tool.

Assessment of Splenic Switch-Off With Arterial Spin Labeling in Adenosine Perfusion Cardiac MRI.

BACKGROUND: In patients with suspected coronary artery disease (CAD), myocardial perfusion is assessed under rest and pharmacological stress to identify ischemia. Splenic switch-off, defined as the stress to rest splenic perfusion attenuation in response to adenosine, has been proposed as an indicator of stress adequacy. Its occurrence has been previously assessed in first-pass perfusion images, but the use of noncontrast techniques would be highly beneficial. PURPOSE: To explore the ability of pseudo-continuous arterial spin labeling (PCASL) to identify splenic switch-off in patients with suspected CAD. STUDY TYPE: Prospective. POPULATION: Five healthy volunteers (age 24.8 ± 3.8 years) and 32 patients (age 66.4 ± 8.2 years) with suspected CAD. FIELD STRENGTH/SEQUENCE: A 1.5-T/PCASL (spin-echo) and first-pass imaging (gradient-echo). ASSESSMENT: In healthy subjects, multi-delay PCASL data (500-2000 msec) were acquired to quantify splenic blood flow (SBF) and determine the adequate postlabeling delay (PLD) for single-delay acquisitions (PLD > arterial transit time). In patients, single-delay PCASL (1200 msec) and first-pass perfusion images were acquired under rest and adenosine conditions. PCASL data were used to compute SBF maps and SBF stress-to-rest ratios. Three observers classified patients into "switch-off" and "failed switch-off" groups by visually comparing rest-stress perfusion data acquired with PCASL and first-pass, independently. First-pass categories were used as reference to evaluate the accuracy of quantitative classification. STATISTICAL TESTS: Wilcoxon signed-rank, Pearson correlation, kappa, percentage agreement, Generalized Linear Mixed Model, Mann-Whitney, Pearson Chi-squared, receiver operating characteristic, area-under-the-curve (AUC) and confusion matrix. SIGNIFICANCE: P value < 0.05. RESULTS: A total of 27 patients (84.4%) experienced splenic switch-off according to first-pass categories. Comparison of PCASL-derived SBF maps during stress and rest allowed assessment of splenic switch-off, reflected in a reduction of SBF values during stress. SBF stress-to-rest ratios showed a 97% accuracy (sensitivity = 80%, specificity = 100%, AUC = 85.2%). DATA CONCLUSION: This study could demonstrate the feasibility of PCASL to identify splenic switch-off during adenosine perfusion MRI, both by qualitative and quantitative assessments. EVIDENCE LEVEL: 2 TECHNICAL EFFICACY: 2.

Multiparametric renal magnetic resonance imaging: A reproducibility study in renal allografts with stable function.

Monitoring renal allograft function after transplantation is key for the early detection of allograft impairment, which in turn can contribute to preventing the loss of the allograft. Multiparametric renal MRI (mpMRI) is a promising noninvasive technique to assess and characterize renal physiopathology; however, few studies have employed mpMRI in renal allografts with stable function (maintained function over a long time period). The purposes of the current study were to evaluate the reproducibility of mpMRI in transplant patients and to characterize normal values of the measured parameters, and to estimate the labeling efficiency of Pseudo-Continuous Arterial Spin Labeling (PCASL) in the infrarenal aorta using numerical simulations considering experimental measurements of aortic blood flow profiles. The subjects were 20 transplant patients with stable kidney function, maintained over 1 year. The MRI protocol consisted of PCASL, intravoxel incoherent motion, and T1 inversion recovery. Phase contrast was used to measure aortic blood flow. Renal blood flow (RBF), diffusion coefficient (D), pseudo-diffusion coefficient (D*), flowing fraction ( f ), and T1 maps were calculated and mean values were measured in the cortex and medulla. The labeling efficiency of PCASL was estimated from simulation of Bloch equations. Reproducibility was assessed with the within-subject coefficient of variation, intraclass correlation coefficient, and Bland-Altman analysis. Correlations were evaluated using the Pearson correlation coefficient. The significance level was p less than 0.05. Cortical reproducibility was very good for T1, D, and RBF, moderate for f , and low for D*, while medullary reproducibility was good for T1 and D. Significant correlations in the cortex between RBF and f (r = 0.66), RBF and eGFR (r = 0.64), and D* and eGFR (r = -0.57) were found. Normal values of the measured parameters employing the mpMRI protocol in kidney transplant patients with stable function were characterized and the results showed good reproducibility of the techniques.

Cardiometabolic characterization in metabolic dysfunction-associated fatty liver disease.

Background: To better understand the patient's heterogeneity in fatty liver disease (FLD), metabolic dysfunction-associated fatty liver disease (MAFLD) was proposed by international experts as a new nomenclature for nonalcoholic fatty liver disease (NAFLD). We aimed to evaluate the cardiovascular risk, assessed through coronary artery calcium (CAC) and epicardial adipose tissue (EAT), of patients without FLD and patients with FLD and its different subtypes. Methods: Cross sectional study of 370 patients. Patients with FLD were divided into 4 groups: FLD without metabolic dysfunction (non-MD FLD), MAFLD and the presence of overweight/obesity (MAFLD-OW), MAFLD and the presence of two metabolic abnormalities (MAFLD-MD) and MAFLD and the presence of T2D (MAFLD-T2D). MAFLD-OW included two subgroups: metabolically healthy obesity (MHO) and metabolically unhealthy obesity (MUHO). The patients without FLD were divided into 2 groups: patients without FLD and without MD (non-FLD nor MD; reference group) and patients without FLD but with MD (non-FLD with MD). EAT and CAC (measured through the Agatston Score) were determined by computed tomography. Results: Compared with the reference group (non-FLD nor MD), regarding EAT, patients with MAFLD-T2D and MAFLD-MUHO had the highest risk for CVD (OR 15.87, 95% CI 4.26-59.12 and OR 17.60, 95% CI 6.71-46.20, respectively), patients with MAFLD-MHO were also at risk for CVD (OR 3.62, 95% CI 1.83-7.16), and patients with non-MD FLD did not have a significantly increased risk (OR 1.77; 95% CI 0.67-4.73). Regarding CAC, patients with MAFLD-T2D had an increased risk for CVD (OR 6.56, 95% CI 2.18-19.76). Patients with MAFLD-MUHO, MAFLD-MHO and non-MD FLD did not have a significantly increased risk compared with the reference group (OR 2.54, 95% CI 0.90-7.13; OR 1.84, 95% CI 0.67-5.00 and OR 2.11, 95% CI 0.46-9.74, respectively). Conclusion: MAFLD-T2D and MAFLD-OW phenotypes had a significant risk for CVD. MAFLD new criteria reinforced the importance of identifying metabolic phenotypes in populations as it may help to identify patients with higher CVD risk and offer a personalized therapeutic management in a primary prevention setting.

Pulmonary Artery Sarcoma: Value of Dual-Energy CT (DECT)-Based Iodine Quantification on Multimodality Workup

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Deep learning-based lesion subtyping and prediction of clinical outcomes in COVID-19 pneumonia using chest CT.

The main objective of this work is to develop and evaluate an artificial intelligence system based on deep learning capable of automatically identifying, quantifying, and characterizing COVID-19 pneumonia patterns in order to assess disease severity and predict clinical outcomes, and to compare the prediction performance with respect to human reader severity assessment and whole lung radiomics. We propose a deep learning based scheme to automatically segment the different lesion subtypes in nonenhanced CT scans. The automatic lesion quantification was used to predict clinical outcomes. The proposed technique has been independently tested in a multicentric cohort of 103 patients, retrospectively collected between March and July of 2020. Segmentation of lesion subtypes was evaluated using both overlapping (Dice) and distance-based (Hausdorff and average surface) metrics, while the proposed system to predict clinically relevant outcomes was assessed using the area under the curve (AUC). Additionally, other metrics including sensitivity, specificity, positive predictive value and negative predictive value were estimated. 95% confidence intervals were properly calculated. The agreement between the automatic estimate of parenchymal damage (%) and the radiologists' severity scoring was strong, with a Spearman correlation coefficient (R) of 0.83. The automatic quantification of lesion subtypes was able to predict patient mortality, admission to the Intensive Care Units (ICU) and need for mechanical ventilation with an AUC of 0.87, 0.73 and 0.68 respectively. The proposed artificial intelligence system enabled a better prediction of those clinically relevant outcomes when compared to the radiologists' interpretation and to whole lung radiomics. In conclusion, deep learning lesion subtyping in COVID-19 pneumonia from noncontrast chest CT enables quantitative assessment of disease severity and better prediction of clinical outcomes with respect to whole lung radiomics or radiologists' severity score.

Transient elastography and serum markers of liver fibrosis associate with epicardial adipose tissue and coronary artery calcium in NAFLD.

Non-alcoholic fatty liver disease (NAFLD) is associated with cardiovascular disease morbimortality. However, it is not clear if NAFLD staging may help identify early or subclinical markers of cardiovascular disease. We aimed to evaluate the association of liver stiffness and serum markers of liver fibrosis with epicardial adipose tissue (EAT) and coronary artery calcium (CAC) in an observational cross-sectional study of 49 NAFLD patients that were seen at Clínica Universidad de Navarra (Spain) between 2009 and 2019. Liver elastography and non-invasive fibrosis markers were used to non-invasively measure fibrosis. EAT and CAC, measured through visual assessment, were determined by computed tomography. Liver stiffness showed a direct association with EAT (r = 0.283, p-value = 0.049) and CAC (r = 0.337, p-value = 0.018). NAFLD fibrosis score was associated with EAT (r = 0.329, p-value = 0.021) and CAC (r = 0.387, p-value = 0.006). The association of liver stiffness with CAC remained significant after adjusting for metabolic syndrome features (including carbohydrate intolerance/diabetes, hypertension, dyslipidaemia, visceral adipose tissue, and obesity). The evaluation of NAFLD severity through liver elastography or non-invasive liver fibrosis biomarkers may contribute to guide risk factor modification to reduce cardiovascular risk in asymptomatic patients. Inversely, subclinical cardiovascular disease assessment, through Visual Scale for CAC scoring, may be a simple and effective measure for patients with potential liver fibrosis, independently of the existence of other cardiovascular risk factors.

Chest CT-assessed comorbidities and all-cause mortality risk in COPD patients in the BODE cohort.

BACKGROUND AND OBJECTIVE: The availability of chest computed tomography (CT) imaging can help diagnose comorbidities associated with chronic obstructive pulmonary disease (COPD). Their systematic identification and relationship with all-cause mortality have not been explored. Furthermore, whether their CT-detected prevalence differs from clinical diagnosis is unknown. METHODS: The prevalence of 10 CT-assessed comorbidities was retrospectively determined at baseline in 379 patients (71% men) with mild to severe COPD attending pulmonary clinics. Anthropometrics, smoking history, dyspnoea, lung function, exercise capacity, BODE (BMI, Obstruction, Dyspnoea and Exercise capacity) index and exacerbations rate were recorded. The prevalence of CT-determined comorbidities was compared with that recorded clinically. Over a median of 78 months of observation, the independent association with all-cause mortality was analysed. A 'CT-comorbidome' graphically expressed the strength of their association with mortality risk. RESULTS: Coronary artery calcification, emphysema and bronchiectasis were the most prevalent comorbidities (79.8%, 62.7% and 33.9%, respectively). All were underdiagnosed before CT. Coronary artery calcium (hazard ratio [HR] 2.09; 95% CI 1.03-4.26, p = 0.042), bronchiectasis (HR 2.12; 95% CI 1.05-4.26, p = 0.036) and low psoas muscle density (HR 2.61; 95% CI 1.23-5.57, p = 0.010) were independently associated with all-cause mortality and helped define the 'CT-comorbidome'. CONCLUSION: This study of COPD patients shows that systematic detection of 10 CT-diagnosed comorbidities, most of which were not detected clinically, provides information of potential use to patients and clinicians caring for them.

Reduction of motion effects in myocardial arterial spin labeling.

PURPOSE: To evaluate the accuracy and reproducibility of myocardial blood flow measurements obtained under different breathing strategies and motion correction techniques with arterial spin labeling. METHODS: A prospective cardiac arterial spin labeling study was performed in 12 volunteers at 3 Tesla. Perfusion images were acquired twice under breath-hold, synchronized-breathing, and free-breathing. Motion detection based on the temporal intensity variation of a myocardial voxel, as well as image registration based on pairwise and groupwise approaches, were applied and evaluated in synthetic and in vivo data. A region of interest was drawn over the mean perfusion-weighted image for quantification. Original breath-hold datasets, analyzed with individual regions of interest for each perfusion-weighted image, were considered as reference values. RESULTS: Perfusion measurements in the reference breath-hold datasets were in line with those reported in literature. In original datasets, prior to motion correction, myocardial blood flow quantification was significantly overestimated due to contamination of the myocardial perfusion with the high intensity signal of blood pool. These effects were minimized with motion detection or registration. Synthetic data showed that accuracy of the perfusion measurements was higher with the use of registration, in particular after the pairwise approach, which probed to be more robust to motion. CONCLUSION: Satisfactory results were obtained for the free-breathing strategy after pairwise registration, with higher accuracy and robustness (in synthetic datasets) and higher intrasession reproducibility together with lower myocardial blood flow variability across subjects (in in vivo datasets). Breath-hold and synchronized-breathing after motion correction provided similar results, but these breathing strategies can be difficult to perform by patients.